ALPR (Automatic License Plate Recognition) system often functions as the core module of “intelligent” transportation infrastructure applications. License plate recognition can be employed to identify a vehicle by automatically reading a license plate utilizing an image processing and character recognition technology. A license plate recognition operation can be performed by locating the license plate in an image, segmenting the characters in the plate, and performing an OCR (Optical Character Recognition) operation with respect to the characters identified.
ALPR system performance can be typically quoted in terms of yield and accuracy where yield represents a percentage of conclusions drawn for a given number of transactions and accuracy represents a percentage of conclusion that are correct. In LPR applications multiple images can be captured as part of a single transaction (e.g. front and rear plate images) in which all or most of the images include the license plate belonging to a target vehicle. Factors such as, for example, camera placement, camera resolution, speed of traffic, and capture trigger reliability dictate a number of images that need to be captured to reliably identify any vehicle passing through a toll zone. The result from the image containing a highest confidence can be typically chosen as a conclusion for the transaction. Such an approach is suboptimal in that information from rejected conclusions can be ignored.
For example, an ALPR error occurs when a valid but low confidence border character is dropped from an output code making an overall confidence of the code higher while incorrect. Imaging conditions change from capture to capture and vehicle to vehicle and parts of the plate code may be easier to read in the rejected conclusions than in the selected conclusion. In particular, the ALPR results from different images can have set of unique distortions, for example, one or more OCR errors, dropped characters, additional invalid characters (e.g. logos, special symbols, or border artifacts interpreted as real characters). Leveraging the LPR results across multiple views of the similar license plate is not an easy task.
FIG. 1 illustrates a view of license plate images 110 and 120 and a table 130 including sample data obtained from a prior art LPR system. The images 110 and 120 represent front and rear license plate images. The plate data shown in Table 130 possesses multiple errors that must be confronted by, for example, an additional character for one of the results and a disagreement between the results at two of the other character locations. Testing the confidence against the threshold leads to an additional “L” being dropped. The remaining characters that are in disagreement can then be selected based on a superior confidence.
Based on the foregoing, it is believed that a need exists for an improved automated license plate recognition system and method. A need also exists for an improved method for enhancing the performance of the automated license plate recognition system utilizing multiple results, as will be described in greater detail herein.